← Back to all news
arXiv Machine Learning

DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting

DiffPTS rethinks the evidence lower bound (ELBO) for diffusion-based probabilistic time‑series forecasting by adopting a Location‑Scale Noise Model, which yields a Gaussian negative log‑likelihood objective and a unified joint training objective. The framework enables end‑to‑end optimization of all components within the ELBO, unlike prior methods that treat estimator training as separate regression tasks. On multiple benchmarks, DiffPTS achieves state‑of‑the‑art performance, reducing CRPS/MSE by over 14.53%/16.55% compared to existing diffusion‑based models.

Related stories

arXiv Machine Learning
Oct 2

Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting

The paper introduces a new diffusion-based approach for probabilistic weather forecasting that incorporates auxiliary conditional denoising tasks. By predicting future states from both clean and corrupted contexts, the model reduces ambiguity and learns conditional distributions across noise levels using a proper scoring rule. Experiments demonstrate improved one‑step forecasting, especially at longer horizons, and better calibration in high‑dimensional global weather scenarios.

By Joonhyeong Park, Giung Nam, Hyungi Lee, Kyunghyun Cho, Byoungwoo Park, Juho Lee
diffusionfine-tuning
More like this →
arXiv Machine Learning
Sep 3

DynG-Diff: A State-Aware Dynamic Guidance Diffusion Framework for Probabilistic Time Series Forecasting

DynG-Diff is a new diffusion-based framework for probabilistic multivariate time‑series forecasting that addresses the challenge of information heterogeneity across variables. It uses a two‑stage training strategy with an unconditional diffusion backbone and introduces a lightweight state‑aware policy network that dynamically adjusts guidance strength based on real‑time variable reliability. The dynamic guidance is mathematically framed as local precision, allowing the model to focus on high‑confidence variables and suppress anomalous noise, leading to competitive performance and robustness on real‑world benchmarks.

By Zhente Zhang, Zhengwei Ni, Wei Fan
diffusionbenchmarks
More like this →
arXiv AI
Jun 10

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.

By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia
diffusionfine-tuningbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 11

Continuous Diffusion Scales Competitively with Discrete Diffusion for Language

The paper revisits the continuous diffusion language model Plaid and introduces RePlaid, aligning its architecture with modern discrete diffusion models. RePlaid achieves a compute gap of only 20× compared to autoregressive models, surpasses Duo with fewer parameters, and outperforms MDLM in over‑trained settings. On OpenWebText, RePlaid sets a new state‑of‑the‑art continuous diffusion perplexity of 22.1 and demonstrates superior generation quality, while theoretical analysis links likelihood‑based training to linear cross‑entropy over time and structured embedding geometries.

By Zhihan Yang, Wei Guo, Shuibai Zhang, Subham Sekhar Sahoo, Yongxin Chen, Arash Vahdat, Morteza Mardani, John Thickstun
llmsragdiffusionbenchmarks
More like this →